/*
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 *    IteratedSingleClassifierEnhancer.java
 *    Copyright (C) 2004 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers;

import weka.core.Instances;
import weka.core.Option;
import weka.core.Utils;

import java.util.Enumeration;
import java.util.Vector;

/**
 * Abstract utility class for handling settings common to meta classifiers that
 * build an ensemble from a single base learner.
 * 
 * @author Eibe Frank (eibe@cs.waikato.ac.nz)
 * @version $Revision: 1.4 $
 */
public abstract class IteratedSingleClassifierEnhancer extends
		SingleClassifierEnhancer {

	/** for serialization */
	private static final long serialVersionUID = -6217979135443319724L;

	/** Array for storing the generated base classifiers. */
	protected Classifier[] m_Classifiers;

	/** The number of iterations. */
	protected int m_NumIterations = 10;

	/**
	 * Stump method for building the classifiers.
	 * 
	 * @param data
	 *            the training data to be used for generating the bagged
	 *            classifier.
	 * @exception Exception
	 *                if the classifier could not be built successfully
	 */
	public void buildClassifier(Instances data) throws Exception {

		if (m_Classifier == null) {
			throw new Exception("A base classifier has not been specified!");
		}
		m_Classifiers = Classifier.makeCopies(m_Classifier, m_NumIterations);
	}

	/**
	 * Returns an enumeration describing the available options.
	 * 
	 * @return an enumeration of all the available options.
	 */
	public Enumeration listOptions() {

		Vector newVector = new Vector(2);

		newVector.addElement(new Option("\tNumber of iterations.\n"
				+ "\t(default 10)", "I", 1, "-I <num>"));

		Enumeration enu = super.listOptions();
		while (enu.hasMoreElements()) {
			newVector.addElement(enu.nextElement());
		}
		return newVector.elements();
	}

	/**
	 * Parses a given list of options. Valid options are:
	 * <p>
	 * 
	 * -W classname <br>
	 * Specify the full class name of the base learner.
	 * <p>
	 * 
	 * -I num <br>
	 * Set the number of iterations (default 10).
	 * <p>
	 * 
	 * Options after -- are passed to the designated classifier.
	 * <p>
	 * 
	 * @param options
	 *            the list of options as an array of strings
	 * @exception Exception
	 *                if an option is not supported
	 */
	public void setOptions(String[] options) throws Exception {

		String iterations = Utils.getOption('I', options);
		if (iterations.length() != 0) {
			setNumIterations(Integer.parseInt(iterations));
		} else {
			setNumIterations(10);
		}

		super.setOptions(options);
	}

	/**
	 * Gets the current settings of the classifier.
	 * 
	 * @return an array of strings suitable for passing to setOptions
	 */
	public String[] getOptions() {

		String[] superOptions = super.getOptions();
		String[] options = new String[superOptions.length + 2];

		int current = 0;
		options[current++] = "-I";
		options[current++] = "" + getNumIterations();

		System.arraycopy(superOptions, 0, options, current, superOptions.length);

		return options;
	}

	/**
	 * Returns the tip text for this property
	 * 
	 * @return tip text for this property suitable for displaying in the
	 *         explorer/experimenter gui
	 */
	public String numIterationsTipText() {
		return "The number of iterations to be performed.";
	}

	/**
	 * Sets the number of bagging iterations
	 */
	public void setNumIterations(int numIterations) {

		m_NumIterations = numIterations;
	}

	/**
	 * Gets the number of bagging iterations
	 * 
	 * @return the maximum number of bagging iterations
	 */
	public int getNumIterations() {

		return m_NumIterations;
	}
}
